Citrine Informatics
Canvas Category Software : Information Technology : Chemical
Citrine Informatics was founded in 2013 and since then has been laser focussed on AI for Materials and Chemicals. Working on projects across material types, formulations, specialty, and commodity chemicals; partnering with commercial, government and academic organizations, Citrine has gained a reputation as the world leader in this field. Awards from CB Insights, the World Economic Foundation, and the Clean Tech group, along with Series B funding from Prelude Ventures and Innovation Endeavors amongst others, and 3 technology patents have further secured Citrine’s position.
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Advancements in Predicting the Fatigue Lifetime of Structural Adhesive Joints
While physics-based models offer the highest accuracy for analyzing these joints, they require meticulous parameter calibration for every new adhesive. For example, consider a fatigue test on a structural adhesive joint with 10 million cycles at a frequency of 10 Hz. These tests are demanding and time-consuming, taking over 10 days to complete. Adding to the challenge is the need for numerous data points to construct a comprehensive fatigue design curve, a fundamental aspect of structural analysis. Given the need to optimize both efficiency and accuracy, engineers and researchers need and pursue innovative solutions.
One path to solution is the integration of Artificial Intelligence (AI) and Machine Learning (ML) into materials science. Recognized for its ability to address complex problems through learning from existing knowledge, AI provides a promising avenue for structural modeling by generating mathematical expressions that capture the interplay of various parameters. We expect that this rationale also applies to the structural modelling of the fatigue behavior of structural adhesive joints, which is the subject of our ongoing research.
This showcase exemplifies our commitment to revolutionizing materials selection and fatigue life prediction for adhesive joints. Leveraging the Citrine Platform [2], we seamlessly apply machine learning methods to integrate experimental datasets with physics-based modeling (based on stress concentration factors). This innovative approach not only significantly elevates the precision of fatigue predictions but also enables the precise selection of optimal adhesives for bonded structures, factoring in various material and geometrical properties, as well as usage conditions.
Closed-loop fully-automated frameworks for accelerating materials discovery
Our work shows that a fully-automated closed-loop framework driven by sequential learning can accelerate the discovery of materials by up to 10-25x (or a reduction in design time by 90-95%) when compared to traditional approaches. We show that such closed-loop frameworks can lead to enormous improvement in researcher productivity in addition to reducing overall project costs. Overall, these findings present a clear value proposition for investing in closed-loop frameworks and sequential learning in materials discovery and design enterprises.
Citrine Informatics Raises $16M in Series C Financing
Citrine Informatics, the leading provider of artificial intelligence software for materials, chemicals, and manufactured product development, announced the successful close of a $16 million Series C funding round. The round was led by Prelude Ventures and Innovation Endeavors, with participation from Drive Catalyst (Far Eastern Group), Alumni Ventures, ISAI Cap Venture, Presidio Ventures, and others.
This latest round of funding will be used to further accelerate the growth and development of Citrine’s AI-driven materials and chemical design platform, which is already in use by leading companies across materials, chemicals, formulated products, and manufacturing industries to improve the efficiency and effectiveness of their product development processes.